Memory hierarchy considerations for cost-effective cluster computing

Using off-the-shelf commodity workstations and PCs to build a cluster for parallel computing has become a common practice. The cost-effectiveness of a cluster computing platform for a given budget and for certain types of applications is mainly determined by its memory hierarchy and the interconnection network configurations of the cluster. Finding such a cost-effective solution from exhaustive simulations would be highly time-consuming and predictions from measurements on existing clusters would be impractical. We present an analytical model for evaluating the performance impact of memory hierarchies and networks on cluster computing. The model covers the memory hierarchy of a single SMP, a cluster of workstations/PCs, or a cluster of SMPs by changing various architectural parameters. Network variations covering both bus and switch networks are also included in the analysis. Different types of applications are characterized by parameterized workloads with different computation and communication requirements. The model has been validated by simulations and measurements. The workloads used for experiments are both scientific applications and commercial workloads. Our study shows that the depth of the memory hierarchy is the most sensitive factor affecting the execution time for many types of workloads. However, the interconnection network cost of a tightly coupled system with a short depth in memory hierarchy, such as an SMP, is significantly more expensive than a normal cluster network connecting independent computer nodes. Thus, the essential issue to be considered is the trade-off between the depth of the memory hierarchy and the system cost. Based on analyses and case studies, we present our quantitative recommendations for building cost-effective clusters for different workloads